SYSTRAN, a global leader in language translation technology, demonstrated two new integrations for SYSTRAN’s offering of Relativity at Ing3nious’ SoCal E-discovery & Information Governance Retreat, November 13-14.
SYSTRAN demonstrated two new integrations, aDiscovery and Anonymizer, for SYSTRAN’s offering of Relativity at the event. The aDiscovery feature will aid in audio discovery by transcribing audio files, detecting the source language and then translating the content. Anonymizer applies rigorous anonymization techniques to the full text and metadata of electronic documents within Relativity.

Anonymization can be used to mask identifying details in documents such as names, addresses, identification numbers, places, amounts and so forth when reading the anonymized documents; however, anonymized documents retain sufficient information for most relevancy reviews. Users also have the ability to “pseudononymize” selected names replacing pre-identified names with chosen pseudonyms on a mass basis to provide another option for privacy protection.

By 2020, Gartner predicts that 80 percent of litigation will involve multiple languages. The features are meant to assist with multi-language or cross-border e-discovery, giving legal teams a cost-effective method to translate files efficiently.

“These new integrations are going to change the way modern day legal teams handle multi-language files during e-discovery by maximizing productivity through automatic translation and Natural Language Processing (NLP),” says Ken Behan, SYSTRAN Vice President of Sales & Marketing. “These methods are far more efficient than manual translation and will save firms time and money during the e-discovery process.”

In addition to the new integrations, SYSTRAN’s offering for Relativity automatically detects languages of files, translates documents that have multiple languages, and bulk translates using the Mass Action feature in Relativity. Organizations using Relativity are also able to support their billing process by accurately reflecting the workload completed.